Importance of the Adsorption Method Used for Obtaining the Nanoparticle Dosage for Asphaltene-Related Treatments
Bibliographic record
Abstract
The primary objective of this study is to show the importance of the adsorption method used in obtaining the nanoparticle dosage for inhibiting/remediating asphaltene-related problems. In this work, two methods for determining the adsorption isotherms for different asphaltenes onto three different types of nanoparticles were evaluated. The adsorption equilibrium of n -C 7 asphaltenes was determined using batch-mode adsorption experiments that were performed in two different ways: (i) by exposing a certain mass of nanoparticles in a fixed volume of liquid with a varying initial concentration of asphaltenes and (ii) by exposing a given amount of asphaltenes in a fixed volume of liquid while varying the dosage of nanoparticles. The results obtained using these two methods were sufficient to determine the type I and III adsorption isotherms, respectively. These differences in behavior in adsorption isotherms can be due complexity of the n -C 7 asphaltenes, which are self-associative molecules that impact directly the interaction between the adsorbate ( i -mers depending on their concentration) and adsorbent. These results were proven through the aggregate size distribution of asphaltenes as estimated by dynamic light scattering (DLS) measurements. The experimental data was well described with the solid–liquid equilibrium (SLE) model. The adsorption isotherms obtained using the second method deviated significantly from those typically reported in the literature. However, this method is a useful tool for determining the required amount of nanoparticles based on the interactions of adsorbate–adsorbate and adsorbate–nanoadsorbent. Indeed, this method is of practical significance because the amount of asphaltenes at reservoir conditions can be considered constant when treatments are performed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".